RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
This addresses the time- and labor-intensive problem of human annotation for evaluating dialog systems, offering a flexible and extensible solution for researchers and developers.
The paper tackles the lack of a standard automatic evaluation metric for open-domain dialog systems by proposing RUBER, an unsupervised method that blends referenced and unreferenced metrics, achieving high correlation with human annotation in experiments on retrieval and generative systems.
Open-domain human-computer conversation has been attracting increasing attention over the past few years. However, there does not exist a standard automatic evaluation metric for open-domain dialog systems; researchers usually resort to human annotation for model evaluation, which is time- and labor-intensive. In this paper, we propose RUBER, a Referenced metric and Unreferenced metric Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance). Our metric is learnable, but its training does not require labels of human satisfaction. Hence, RUBER is flexible and extensible to different datasets and languages. Experiments on both retrieval and generative dialog systems show that RUBER has a high correlation with human annotation.